Detecting Rumors on Social Media Based on a CNN Deep Learning Technique

Abstract

Currently, it is easy to create content and share it via social media platforms such as Twitter, Facebook, and Sina Weibo. However, some problems can occur when the shared content includes untrustworthy or misleading information. Thus, researchers from different domains have tried to investigate the impact of rumors on the global community. Several machine learning approaches have been used to detect rumors at their early stage. However, the achieved accuracies demonstrate that the existing state-of-the-art rumor detection approaches still require improvement. In this paper, we propose a deep learning model based on a conventional neural network (CNN) to detect rumors spreading on Twitter. Several experiments were conducted to find the best hyperparameter settings to improve the model's performance. We compared our results with other relevant rumor detection approaches that used the same publicly available benchmark dataset to demonstrate our model's performance regarding the accuracy, precision, recall and f -measure. The results show that our CNN model outperforms all the existing approaches and achieves the best balance of recall and precision.

HANN: Hybrid Attention Neural Network for Detecting Covid-19 Related Rumors

“…LSTM holds the connection between various words in these sequences. Al-Sarem et al [24] [26]. It leverages a GCN to learn the patterns of rumor propagation.…”

“…However, a number of studies used embedding techniques and shown remarkable results. Embedding techniques can capture word associations and improve prediction accuracy [24,26,29]. Word embedding is one of the most popular techniques to represent text vocabulary.…”

Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

“…Recently, artificial intelligence (AI) tools, such as machine learning (ML) and deep learning (DL), along with the development of other techniques, such as the Internet of Things (IoT), has attracted several researchers because of their efficiency in various fields like rumor detection in social media [2], natural language processing [3,4], plant disease detection [5,6], smart energy economy and management [7][8][9], civil engineering systems [10,11], and educational system analysis and assessment [12,13].…”

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

“…Artificial Intelligence (AI) techniques have been widely used in many applications such as handwriting recognition [5], rumors or fake news detection in social media [6,7], medical diagnosis support systems (MDSS) [8,9], prediction of patients with heart disease [10][11][12][13][14], and MRI image segmentation [15][16][17][18][19][20][21]. Particularly in the medical field, these techniques have been proved invaluable in predicting…”